Data fusion method for household appliance production quality safety traceability

Abstract

The invention relates to the technical field of data traceability management, in particular to a data fusion method for household appliance production quality safety traceability. The method comprises the following steps of periodically and synchronously collecting hardware component installation state data, assembly equipment operation parameters and assembly material attribute parameters in the assembly process of the household appliance, and fusing the data into a real-time assembly data set; performing deviation calculation on the real-time assembly data set and the standard assembly data set, determining an assembly deviation value, and marking the assembly deviation value as a quality characteristic parameter; and adjusting the operation state of the assembly equipment according to the quality characteristic parameters, and synchronously reading a feedback signal of the assembly equipment in the adjustment process. According to the invention, through a data fusion technology and a data association tracing technology, a quality monitoring and abnormity tracing system covering the whole household appliance assembling process is formed, so that the accuracy, stability and traceability of household appliance production quality safety management are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data traceability management, and particularly relates to a data fusion method for quality and safety traceability of household appliance production. BACKGROUND

[0002] The multi-component integration characteristics of household appliances increase the complexity of assembly, and problems such as looseness and misplacement may occur in the assembly process, affecting the performance of the device. However, assembly quality problems are difficult to be completely discovered by automatic detection equipment, and need to be checked and tested manually. In large-scale production, the efficiency and accuracy of manual inspection are limited, and it is difficult to ensure the assembly quality of each device; once the device fails, it is difficult to trace to the specific assembly link. In addition, in the face of the complex assembly scene of multi-component integration of household appliances, the existing technology does not establish a synchronous collection and fusion mechanism for hardware component installation state data, assembly equipment operating parameters and assembly material attribute parameters, and cannot form real-time assembly data groups covering the whole process of multi-component assembly. This leads to the fact that problems such as looseness and misplacement occurring in the assembly process cannot be captured by automatic detection equipment through data correlation analysis, and can only rely on manual inspection and testing, which directly limits the automatic identification ability of assembly quality problems. SUMMARY

[0003] Therefore, it is necessary to provide a data fusion method for quality and safety traceability of household appliance production to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a data fusion method for quality and safety traceability of household appliance production is applied to an assembly link in the production process of household appliances, the household appliances are composed of multiple hardware components, the assembly link involves multiple assembly stations, and the method comprises the following steps:

[0005] Step S1: In the assembly process of household appliances, periodically synchronously collect hardware component installation state data, assembly equipment operating parameters and assembly material attribute parameters, and fuse them into real-time assembly data groups;

[0006] Step S2: Calculate the deviation of the real-time assembly data groups and the standard assembly data groups, determine the assembly deviation amount, and mark the assembly deviation amount as a quality characteristic parameter;

[0007] Step S3: Adjust the operating state of the assembly equipment according to the quality characteristic parameter, and synchronously read the feedback signal of the assembly equipment in the adjustment process; determine the equipment operating deviation amount according to the difference between the feedback signal and the parameters in the real-time assembly data groups;

[0008] Step S4: Compare the operating deviation amount with a preset operating deviation threshold, and record the comparison result in real time; if the comparison result is within the preset assembly range, it is determined that the assembly process is in a normal state, and the assembly condition is maintained until the end of the production cycle.

[0009] Step S5: If not within the preset assembly range, determine that the assembly process is in an abnormal state, immediately stop the assembly process and record abnormal information; associate the abnormal information with the corresponding hardware components and assembly station, and generate a household appliance production traceability record.

[0010] The beneficial effects of the present application are:

[0011] 1) The method breaks through the traditional single data collection mode, uses multi-source data synchronous collection technology, organically integrates hardware component installation state data, assembly equipment operating parameters and assembly material attribute parameters to form a real-time assembly data set; this innovative data fusion strategy ensures the deep correlation of different dimension data in the assembly process, providing a comprehensive and accurate data basis for subsequent quality analysis. By constructing a deviation calculation model of real-time assembly data set and standard assembly data set, the assembly deviation is accurately marked as a quality characteristic parameter, realizing the quantitative expression of assembly quality difference, providing an innovative quantitative index for quality control, and improving the scientificity and accuracy of quality analysis.

[0012] 2) Based on the quantitative quality characteristic parameter, the method innovatively uses adaptive device control technology to dynamically adjust the operating state of the assembly equipment; during the adjustment process, real-time feedback signal analysis technology is used to accurately determine the equipment operating deviation by comparing the feedback signal of the assembly equipment with the parameters in the real-time assembly data set, realizing closed-loop control of the equipment adjustment process. This intelligent equipment control mechanism significantly improves the accuracy and real-time performance of equipment operating state adjustment. At the same time, by comparing the operating deviation with the preset operating deviation threshold, an intelligent monitoring system for the assembly process state is constructed, and when the comparison result is within the preset assembly range, the assembly process is automatically maintained in stable operation, ensuring the efficiency and stability of the assembly process and improving the stability and controllability of the production process.

[0013] 3) When the operating deviation exceeds the preset assembly range, the method immediately starts the abnormal response mechanism, and innovatively uses data correlation tracing technology to accurately associate the abnormal information with the corresponding hardware components and assembly station. By constructing a household appliance production traceability record system, the whole chain of abnormal assembly process is traced, providing a clear data path for root cause analysis of quality problems; this innovative abnormal traceability mode not only can quickly locate the link where the quality problem occurs, but also can provide strong data support for subsequent quality improvement and preventive measures, improving the accuracy and effectiveness of household appliance production quality and safety management. BRIEF DESCRIPTION OF DRAWINGS

[0014] Fig. 1 is a schematic diagram of the step flow of the data fusion method for household appliance production quality and safety traceability.

[0015] Fig. 2 To produce assembly equipment device map for household appliance;

[0016] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments in conjunction with the drawings. DETAILED DESCRIPTION

[0017] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] In addition, the drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0019] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] To achieve the above-mentioned purpose, please refer to Figs. 1-2 A data fusion method for household appliance production quality safety traceability is applied to the assembly link in the household appliance production process. The household appliance is composed of multiple hardware components, and the assembly link involves multiple assembly stations. The method comprises the following steps:

[0021] Step S1: In the household appliance assembly process, periodically and synchronously collect hardware component installation state data, assembly equipment operating parameters and assembly material attribute parameters, and fuse them into real-time assembly data groups;

[0022] Preferably, the hardware component installation state data collected in step S1 is specifically:

[0023] Recognize the plate-shaped component, the columnar component and the frame-type component of the assembly hardware component type;

[0024] When the plate-shaped component is recognized, the collection operation execution sequence is to collect the alignment data of the edge mounting hole first, and then collect the plug-in state data of the surface interface;

[0025] When the columnar component is recognized, the collection operation execution sequence is to collect the axial mounting depth data first, and then collect the fit degree data of the radial circumference surface;

[0026] When the frame-type component is recognized, the collection operation execution sequence is to collect the gap width data of the splicing part first, and then collect the horizontal height difference data of the four corners;

[0027] Multiple continuous collections are performed on each collection point of each hardware component, and the average value of the multiple collection data is taken as the effective data of the collection point to determine the hardware component installation state data.

[0028] In the embodiment of the application, when the hardware component enters the assembly station, the component type is recognized by a visual recognition device, and the plate-shaped component, the columnar component and the frame-type component are distinguished; wherein the shooting range of the visual recognition device covers the whole component, the sampling frequency is set to 1 time / s, and the recognition accuracy is controlled within ±2mm to ensure the accuracy of the component type judgment.

[0029] In one operation step of the embodiment of the application, when the plate-shaped component (such as the control panel and the mainboard of a household appliance) is recognized, the following operations are performed:

[0030] 1) Collect the alignment data of the edge mounting hole: deploy a laser alignment sensor directly above the hole, the vertical distance between the sensor and the hole is kept at 50cm, emit a laser beam through the hole during each collection, record the coordinate deviation value of the laser reflection point, and continuously collect 5 times with an interval of 0.5 seconds each time. For example, the 5 collection deviation values of a certain hole are 0.1mm, 0.2mm, 0.1mm, 0.3mm and 0.2mm, and the average value 0.18mm is taken as the alignment effective data of the hole.

[0031] 2) Collect the plug-in state data of the surface interface: install an industrial camera on the side of the interface, shoot the side image after the interface is plugged in, the image resolution is 1280×720 pixels, and continuously shoot 3 times with an interval of 1 second each time. Through image analysis, the average value of the 3 times of the coincidence area ratio of the interface plug and the socket is taken as the plug-in state effective data. For example, the 3 times of the coincidence area ratio are 98%, 97% and 99%, and the average value 98% is the effective data of the interface.

[0032] In one operation step of the embodiment of the present application, when identified as a columnar component (such as a support column of a household appliance, a connecting shaft), the following operations are performed:

[0033] 1) Collect axial installation depth data: install a displacement sensor directly above the component in the axial direction, set the initial distance between the sensor probe and the top end of the component to 100 mm, record the real-time distance between the sensor and the top end of the component each time data is collected, collect data continuously for 4 times with an interval of 0.5 seconds each time. For example, the 4 distance values are 85.2 mm, 85.3 mm, 85.1 mm, and 85.4 mm, and the average value 85.25 mm is the effective data of the axial installation depth (installation depth = initial distance - real-time distance, i.e. 14.75 mm).

[0034] 2) Collect the fit degree data of the radial circumferential surface: evenly arrange 3 pressure sensors on the radial outer side of the component, keep the contact pressure between the sensors and the surface of the component at 5N, collect the pressure feedback value of each sensor each time data is collected, collect data continuously for 4 times with an interval of 1 second each time. Calculate the average value of the 4 feedback values of each sensor, and then take the arithmetic mean of the 3 average values as the effective data of the fit degree. For example, the average pressure values of the 3 sensors are 4.8N, 5.0N, and 5.2N, and the total average value 5.0N is the effective data of the fit degree.

[0035] In one operation step of the embodiment of the present application, when identified as a frame type component (such as a housing frame of a household appliance, an internal support frame), the following operations are performed:

[0036] 1) Collect the gap width data of the spliced part: install a laser width measuring instrument on both sides of the spliced joint, keep the horizontal distance between the width measuring instrument and the gap at 30 cm, evenly select 5 measurement points along the gap, collect data continuously for 3 times at each point with an interval of 0.5 seconds each time. Take the average value of the 3 data at each point as the gap width of that point, and then aggregate the average values of the 5 points as the effective data of the spliced part. For example, the average widths of the 5 points of a spliced joint are 0.2 mm, 0.3 mm, 0.2 mm, 0.4 mm, and 0.3 mm, and the total average value 0.28 mm is the effective data of the gap width.

[0037] 2) Collect the horizontal height difference data of the four corners: place a height sensor at each corner, keep the height sensor measuring reference surface and the assembly platform horizontal, collect height values continuously for 3 times at each corner with an interval of 1 second each time. Calculate the height difference between each corner and the reference surface, take the average value of the 3 difference values as the effective data of that corner, and then calculate the difference between the maximum and minimum effective data of the four corners as the final effective data of the horizontal height difference. For example, the average height differences of the four corners are 0.1 mm, 0.2 mm, 0.1 mm, and 0.3 mm, and the maximum difference 0.2 mm is the effective data of the horizontal height difference.

[0038] It should be noted that if the deviation of single data from the average value in the multiple collection data of a collection point exceeds ±0.3mm (numerical type) or ±5% (proportion / pressure type), the data is rejected and re-collected until the consecutive valid data meets the number requirement.

[0039] Preferably, the collected assembly equipment operation parameters in step S1 are specifically:

[0040] According to the main operation type of the assembly equipment in the current assembly process, when the main operation type is to provide driving force to drive the movement of the part, it is determined that the assembly equipment is a power supply type equipment; when the main operation type is to directly perform part grabbing, positioning or fastening operation, it is determined that the assembly equipment is a mechanical execution type equipment.

[0041] If it is determined to be a power supply type equipment, the voltage value is collected at the peak position of each power supply cycle after the equipment is started, and the current value is collected at the valley position; the collection time points of adjacent cycles are adjusted in an equal ratio increasing manner, and the temperature gradient data of the power supply line are recorded synchronously;

[0042] If it is determined to be a mechanical execution type equipment, when performing linear motion, the speed value is collected at 1 / 4, 1 / 2 and 3 / 4 positions of the stroke respectively; when performing rotary motion, the torque value is collected every interval of π / 4 radian according to the rotation angle; after each action cycle is completed, an equal calibration force is applied for measurement, and the angle deviation value of the mechanical arm joint is collected.

[0043] In the embodiment of the present application, before the assembly process is started, the job list of the current assembly process is called through the process information system deployed in the assembly line, and the list contains the preset operation description of the assembly equipment required by the process (such as “drive the conveyor belt to convey the part” “grab the mainboard and position the installation”).

[0044] Please refer to Fig. 2 If the main function of the equipment described in the job list is “provide driving force” “output power to drive the movement of the part”, and the equipment outputs power through components such as motors, hydraulic pumps, etc. to indirectly drive the movement of the assembly part (such as conveyor belt, conveying roller) without direct contact with the assembly part, it is determined to be a power supply type equipment 101. If the main function of the equipment described in the job list is “grab the part” “position the installation” “tighten the screw”, and the equipment directly contacts the assembly part through mechanical arm, gripper, screwdriver, etc. to complete the grabbing, aligning the installation position or fastening operation, it is determined to be a mechanical execution type equipment 102. If the equipment has both types of operations (such as a conveying machine with grabbing function), the “main operation type” marked in the job list is used as the reference.

[0045] In one operation step of the embodiment of the present application, after the device is started, the power supply line is accessed through the voltage sensor and the current sensor. The power supply period is set to 20 ms (corresponding to 50 Hz of power frequency), and in each period, when the voltage waveform reaches the peak (such as the 90° phase point of a sine wave), the voltage sensor collects a voltage value once, with the precision reserved to 0.1 V; when the current waveform reaches the trough (such as the 270° phase point of a sine wave), the current sensor collects a current value once, with the precision reserved to 0.01 A. The collection time points of adjacent periods are adjusted in an equal ratio increasing manner, and the increasing ratio is set to 1.2.

[0046] It should be noted that the peak collection time of the first period is 10 ms, the peak collection time of the second period is 10 x 1.2 = 12 ms, the peak collection time of the third period is 12 x 1.2 = 14.4 ms, and so on; the current collection time points are adjusted in the same ratio. The temperature gradient data of the power supply line are collected synchronously using the temperature sensor, and one temperature sensor is arranged at the input end, the middle section, and the output end of the power supply line, with the sampling frequency consistent with the voltage and current collection frequency. After each collection, the temperature difference between the adjacent two points (such as the temperature difference between the input end and the middle section, and the temperature difference between the middle section and the output end) is calculated as the temperature gradient data, with the precision reserved to 0.1℃.

[0047] In one operation step of the embodiment of the present application, when the device performs linear motion (such as the movement of a pushing component along a guide rail driven by a pushing cylinder), the motion stroke is determined through the displacement sensor. One trigger point is arranged at each of the 1 / 4, 1 / 2, and 3 / 4 positions of the stroke, and when the device moves to the trigger point, the speed sensor collects an instantaneous speed value once, with the precision reserved to 0.1 m / s. For example, when the total stroke is 200 mm, the speed is collected at the positions of 50 mm, 100 mm, and 150 mm. When the device performs rotary motion (such as the rotary action of a screw tightening mechanism), the rotary angle is monitored through the angle encoder, and the torque sensor collects a torque value once every π / 4 radian (i.e., 45°), with the precision reserved to 0.1 N·m. For example, starting from 0°, the torque is collected at the positions of 45°, 90°, 135°, and so on.

[0048] It should be noted that after each action cycle (such as the completion of one cycle of grabbing and placing by a mechanical arm), the calibration device is used to apply an equal calibration force (such as a force of 5 N) to the joint of the mechanical arm, and the actual angle of the joint is collected through the angle sensor. The angle deviation value (actual angle-theoretical angle) is calculated by comparing the actual angle with the theoretical standard angle, with the precision reserved to 0.1°.

[0049] Preferably, the collected material attribute parameters in step S1 are specifically as follows:

[0050] The surface feature point arrangement rule of the assembly material is identified to determine the assembly material type, when the line direction of adjacent feature points presents at least three different angles and has no obvious row and column alignment feature, it is determined that the material is dot matrix distributed material; when the line direction of adjacent feature points presents only two directions perpendicular to each other and forms a regular grid, it is determined that the material is chessboard distributed material;

[0051] If it is determined that the material is dot matrix distributed material, attribute parameters are sequentially collected from the edge feature points to the center feature points of the material;

[0052] If it is determined that the material is chessboard distributed material, attribute parameters of odd row feature points are collected along the horizontal direction of the chessboard, and attribute parameters of even column feature points are collected along the vertical direction of the chessboard;

[0053] In the collection process of each row or each column of the chessboard distributed material, an interval skip collection mode is used, in which the second feature point is skipped after the first feature point is collected, the fourth feature point is skipped after the third feature point is collected, and so on until the collection of the row or the column is completed.

[0054] In the embodiment of the application, when the assembly material enters the detection station, the surface of the material is photographed by an industrial camera to obtain a distribution image of the surface feature points, the image resolution is set to 2048*1536 pixels, the shooting distance is kept at 30 cm, and the feature points are ensured to be completely presented. The image is transmitted to an image analysis device to identify and mark the coordinate positions of all the feature points (taking the lower left corner of the material as the coordinate origin, and the unit is mm).

[0055] For any two adjacent feature points, the included angle (range 0°-180°) between the line and the horizontal direction is calculated through the coordinate difference value, and is accurate to 1°. If there are at least three different angles (such as 30°, 60°, 90°) in the statistical angles, and all the feature points have no obvious row and column alignment rule (the number of feature points in any row or column is not more than 30% of the total number), it is determined that the material is dot matrix distributed material; the feature point line angle of a certain material surface contains 25°, 50°, 75° and 100°, and no more than three consecutive feature points are arranged in a straight line, that is, it belongs to this type. If the line direction of adjacent feature points presents only two directions perpendicular to each other, that is, 0° (horizontal) and 90° (vertical), and the feature points form a regular grid (the spacing deviation of feature points in each row and each column is not more than 0.5 mm), it is determined that the material is chessboard distributed material; the feature points are arranged with a horizontal spacing of 10 mm and a vertical spacing of 8 mm, and each row and each column is aligned, that is, it belongs to this type.

[0056] In one operation step of the embodiment of the present application, first, edge feature points (feature points with a distance of less than or equal to 5 mm from the edge of the material) are identified, and are sequentially labeled as P1, P2, P3… in a clockwise direction; then, a center feature point (a point with the smallest average distance from all edge feature points) is determined and labeled as Pc; the collection sequence is P1→P2→…→Pc, and 3 attribute parameters (such as material hardness and surface roughness) are collected for each feature point, with an interval of 1 second between each collection.

[0057] In one implementation of the embodiment of the present application, the row numbers (1 row, 2 row…) are labeled in a left-to-right order, and odd-numbered rows (1 row, 3 row, 5 row…) are selected; an interval skip collection method is used for each row: the first feature point is collected, the second is skipped, the third is collected, the fourth is skipped, and so on until the end of the row.

[0058] In one implementation of the embodiment of the present application, the column numbers (1 column, 2 column…) are labeled in a front-to-back order, and even-numbered columns (2 column, 4 column, 6 column…) are selected; an interval skip collection method is also used for each column, with the same rules as row collection. 2 attribute parameters are collected for each feature point, with an interval of 0.5 seconds between each collection.

[0059] It should be noted that the determination of the adjacent relationship of the feature points is based on a distance of less than or equal to 10 mm, and feature points beyond this range are not considered adjacent; when calculating the angle, if the difference between two angles is less than or equal to 5°, the angles are considered the same. During the collection process, if the deviation between the data collected by a feature point and the adjacent point exceeds 10%, the feature point needs to be collected again.

[0060] Step S2: Calculate the deviation between the real-time assembly data set and the standard assembly data set to determine the assembly deviation, and mark the assembly deviation as a quality characteristic parameter;

[0061] Preferably, step S2 includes the following steps:

[0062] Step S21: Split the real-time assembly data set into an installation state data sub-set, an assembly equipment operation parameter sub-set, and an assembly material attribute parameter sub-set according to the data types, and split the standard assembly data set into three standard sub-sets at the same time;

[0063] Step S22: For the installation state data sub-set and the corresponding standard sub-set, calculate the deviation in sequence according to the assembly order of the hardware components, first calculate the independent installation deviation of each component, and then calculate the cooperative installation deviation of adjacent components;

[0064] Step S23: For the assembly equipment operation parameter sub-set and the corresponding standard sub-set, calculate the deviation by segmenting according to the time nodes of equipment operation, and calculate the parameter fluctuation deviation in the equipment start-up stage, stable operation stage, and pre-shutdown stage, respectively.

[0065] Step S24: For the assembly material attribute parameter sub-group and the corresponding standard sub-group, calculate the deviation according to the assembly sequence of the material, first calculate the initial attribute deviation when not assembled, and then calculate the dynamic attribute deviation during the assembly process;

[0066] Step S25: Integrate the calculation results of the above three types of deviations, weight the assembly deviation according to the installation state data deviation accounting for 40%, the assembly equipment running parameter deviation accounting for 30%, and the assembly material attribute parameter deviation accounting for 30%, and mark the assembly deviation as a quality characteristic parameter.

[0067] In one operation step of an embodiment of the present application, the installation state data sub-group includes the installation position coordinates (accurate to 0.1 mm) of the hardware components, the fastening torque value (accurate to 0.1 N·m), the interface plugging depth (accurate to 0.1 mm), etc.; for example, the installation state data of a certain mainboard is “coordinates (100.2, 50.5) mm, screw torque 3.2 N·m, interface plugging depth 8.0 mm”. The assembly equipment running parameter sub-group includes the equipment running speed (such as a conveyor belt 2.5 m / min), the working pressure (such as a pneumatic tool 0.6 MPa), the running temperature (such as a motor 45℃), etc. The assembly material attribute parameter sub-group includes the size specification of the material (such as a plastic part length 50.0 mm), the material hardness (such as a metal part HRC 30), the surface roughness (such as Ra 1.6 μm), etc. The standard assembly data group is divided into three standard sub-groups according to the above classification, and the standard values are set according to the household appliance assembly process file, for example, the standard installation torque is 3.0 ± 0.2 N·m.

[0068] In another operation mode of an embodiment of the present application, the hardware components are processed in sequence according to the assembly sequence (such as first installing the bottom plate, then installing the support, and finally installing the panel). The independent installation deviation of each component is calculated: for example, the real-time installation coordinates of the bottom plate are (200.3, 100.1) mm, the standard coordinates are (200.0, 100.0) mm, and the deviation is “X direction + 0.3 mm, Y direction + 0.1 mm”; the real-time torque of the screw is 3.4 N·m, the standard torque is 3.0 N·m, and the deviation is +0.4 N·m. The cooperative installation deviation of adjacent components is calculated: for example, the real-time gap between the support and the bottom plate is 0.3 mm, the standard gap is 0.2 mm, and the deviation is +0.1 mm; the real-time parallelism value of the panel and the support is 0.5 mm / m, the standard value is 0.3 mm / m, and the deviation is +0.2 mm / m.

[0069] In another operation mode of the embodiment of the application, the device startup phase (0-30 seconds after startup): collect parameters every 5 seconds, calculate the deviation of the real-time value from the standard startup curve. For example, the real-time speed of the conveyor belt is 1.2 m / min at the 10th second of the startup phase, the standard speed is 1.0 m / min, and the deviation is +0.2 m / min. The stable operation phase (30 seconds after startup to 30 seconds before shutdown): collect parameters every 10 seconds, calculate the deviation of the real-time value from the standard stable value. For example, the real-time current of the welding device is 150 A during stable operation, the standard current is 145 A, and the deviation is +5 A. The pre-shutdown phase (30 seconds before shutdown): collect parameters every 5 seconds, calculate the deviation of the real-time value from the standard shutdown curve. For example, the real-time speed of the mechanical arm is 0.3 m / s at the 10th second before shutdown, the standard speed is 0.2 m / s, and the deviation is +0.1 m / s. The parameter fluctuation deviation is the average of the absolute values of all deviations in each phase.

[0070] In another operation mode of the embodiment of the application, the assembly sequence of the material is processed according to the use sequence (such as first using the shell, then using the internal support, and finally using the connecting piece). The initial attribute deviation when not assembled is calculated: for example, the real-time length of the shell when not assembled is 120.2 mm, the standard length is 120.0 mm, and the deviation is +0.2 mm. The dynamic attribute deviation during assembly is calculated: for example, the real-time hardness HRC of the support during assembly is 29, the standard hardness HRC is 30, and the deviation is -1 HRC; the real-time roughness Ra of the connecting piece after assembly is 1.8 μm, the standard roughness Ra is 1.6 μm, and the deviation is +0.2 μm.

[0071] Step S3: adjusting the running state of the assembly device according to the quality characteristic parameters, and synchronously reading the feedback signals of the assembly device during the adjustment; determining the device running deviation amount according to the difference between the feedback signals and the parameters in the real-time assembly data set;

[0072] Preferably, the step S3 of adjusting the running state of the assembly device according to the quality characteristic parameters, and synchronously reading the feedback signals of the assembly device during the adjustment comprises:

[0073] identifying the hardware component type associated with the quality characteristic parameters, when the plate-shaped component is associated, adjusting the running state of the assembly device to be directed to the transmission part related to the assembly of the plate-shaped component; when the columnar component is associated, adjusting to be directed to the positioning part related to the assembly of the columnar component; when the frame-type component is associated, adjusting to be directed to the splicing part related to the assembly of the frame-type component;

[0074] for the transmission part associated with the plate-shaped component, adjusting in the order of first reducing the running speed of the conveyor belt and then reducing the spacing of the clamping device, and maintaining the current state after each adjustment until the delivery cycle of one plate-shaped component is completed;

[0075] For the positioning components associated with the columnar components, the adjustment is performed in the order of first increasing the rotation platform speed, and then increasing the pressing device pressure, and after each adjustment, the rotation platform is stopped until it completes a half rotation;

[0076] For the splicing components associated with the frame body components, the adjustment is performed in the order of first slowing down the splicing speed, and then adjusting the splicing alignment accuracy, and after each adjustment, the current state is maintained until the splicing operation of one frame body edge is completed.

[0077] Before the adjustment is started, the initial feedback signal of the assembly equipment is read;

[0078] When the transmission component is adjusted, the feedback signal is read at the transmission belt turning node, when the positioning component is adjusted, the feedback signal is read at the moment when the pressing device contacts the columnar component, and when the splicing component is adjusted, the feedback signal is read at the frame body edge abutting overlap.

[0079] In the embodiment of the present application, the type identifier of the associated hardware component is extracted from the storage entry of the quality characteristic parameter (such as “deviation amount X-time stamp-workstation-plate-shaped component”). The system presets three types of identification rules: when the entry contains the keyword “plate-shaped”, it corresponds to a plate-shaped component (such as a circuit board or a panel); when it contains the keyword “columnar”, it corresponds to a columnar component (such as a support column or a shaft part); and when it contains the keyword “frame body”, it corresponds to a frame body component (such as a housing frame or a support frame). If the identification is ambiguous, the installation state data subgroup associated with the parameter (such as the hole alignment data of the plate-shaped component or the gap data of the frame body component) is called to assist in the determination.

[0080] In another implementation manner of the embodiment of the present application, the first step is to reduce the running speed of the transmission belt. When the original speed is 2 m / min, it is adjusted to 1.5 m / min (the adjustment amplitude is 75% of the original speed), and the setting is completed through the speed adjustment knob of the equipment control panel, and the precision is retained to 0.1 m / min. The second step is to reduce the distance between the clamping devices. When the original distance is 100 mm, it is adjusted to 80 mm (reduced by 20%), and the adjustment is realized by rotating the adjustment screw of the clamping device. Every rotation of 1 circle corresponds to a change of 2 mm in the distance, and 10 rotations are required. After each adjustment, the current speed and distance are kept unchanged until the delivery cycle of one plate-shaped component is completed (from the time when the component enters the inlet of the transmission belt to the time when it leaves the outlet, such as 20 seconds).

[0081] In another implementation of the embodiment of the application, the first step is to increase the rotation speed of the rotating platform. The original rotation speed is 30 rpm, which is adjusted to 40 rpm (an increase of 33%). The speed is adjusted by the gear of the device speed controller, with each gear corresponding to an increase of 5 rpm. The second step is to increase the pressure of the pressing device. The original pressure is 5 N, which is adjusted to 7 N (an increase of 40%). The pressure is adjusted by the air pressure valve, with each 1 / 4 rotation of the valve corresponding to an increase of 0.5 N. The device is rotated by 1 full rotation. After each adjustment, the rotating platform is stopped and waits for the completion of half a rotation (1 second at the original speed and 0.75 seconds after adjustment). The stop signal is triggered by the angle sensor on the platform.

[0082] In another implementation of the embodiment of the application, the first step is to slow down the splicing speed. The original speed is 10 mm / s, which is adjusted to 8 mm / s (a decrease of 20%). The speed is adjusted by the speed regulator of the pushing motor, with each scale corresponding to an increase of 1 mm / s. The second step is to adjust the splicing accuracy. The original splicing error is ±0.5 mm, which is adjusted to ±0.3 mm (a decrease of 40%). The accuracy is adjusted by the scale dial of the fine adjustment mechanism, with each scale corresponding to an increase of 0.05 mm. After each adjustment, the current speed and accuracy parameters are maintained until the splicing operation of one frame edge is completed (for example, a single frame length of 200 mm, which takes 25 seconds at a speed of 8 mm / s).

[0083] It should be noted that the initial feedback signals of the assembly device are read through the device signal interface, including the current speed of the conveyor belt (e.g., 2.0 m / min), the rotation speed of the rotating platform (e.g., 30 rpm), the pressure of the splicing device (e.g., 5 N), etc., which are recorded as initial state values. Position sensors are arranged at the turning nodes of the conveyor belt (e.g., at 90° corners), and when the plate-shaped component triggers the sensor, the running current of the conveyor belt (e.g., 1.2 A) and the clamping device pressure (e.g., 3 N) at that time are read as feedback signals. At the moment when the pressing device contacts the columnar component (when the pressure sensor value jumps from 0 to ≥1 N), the real-time rotation speed of the rotating platform (e.g., 40 rpm) and the displacement of the pressing device (e.g., 5 mm) are read as feedback signals. At the frame edge butt joint coincidence position (when the position sensor detects that the distance between the two side frames is ≤0.1 mm), the running voltage of the pushing motor (e.g., 220 V) and the splicing error value (e.g., 0.3 mm) are read as feedback signals.

[0084] Preferably, in step S3, the difference between the feedback signals and the parameters in the real-time assembly data set is used to determine the device operation deviation.

[0085] The feedback signals are divided into start phase signal segments, running phase signal segments, and pause phase signal segments according to the time nodes of the assembly process. The parameters in the real-time assembly data set are also divided into three phases.

[0086] For the start-up phase, the difference between the feedback signal segment and the corresponding stage installation state data is calculated first, and then the difference between the signal segment and the corresponding stage assembly equipment operating parameter is calculated, both of which are sequentially compared according to the peak points of signal fluctuation;

[0087] For the running phase, according to the assembly sequence of hardware components, the difference between the feedback signal segment and the real-time assembly data parameter of each component assembly is calculated in sequence, and the material attribute parameter difference is compared first, and then the installation state data difference is compared;

[0088] For the pause phase, the difference between the feedback signal segment and the last set of real-time assembly data parameters before the pause is calculated, and when comparing, the stable value part of the signal is extracted first, and then the static value part of the data parameter is extracted;

[0089] The difference calculation results of the three phases are fused according to the proportions of 20% for the start-up phase, 60% for the running phase, and 20% for the pause phase, to obtain the equipment running deviation.

[0090] In one operation process of an embodiment of the present application, from the start of the device being powered on and receiving a start-up instruction to the end of the first hardware component entering the assembly station. For example, after pressing the start button, the conveyor belt starts to run until the first plate-shaped component is transported to the positioning point, and this process lasts for 15 seconds, which is recorded as the start-up phase; from the start of the first component entering the assembly station to the end before the device receives a pause instruction. For example, the process of sequentially completing positioning, fastening, detection and other operations lasts for 180 seconds, which is recorded as the running phase; from the start of the device receiving the pause instruction to the end of all moving parts completely stopping. For example, after pressing the pause button, the conveyor belt gradually slows down to a stop, which lasts for 10 seconds, which is recorded as the pause phase.

[0091] In another operation process of an embodiment of the present application, the waveform of the start-up signal segment (such as the current signal) is analyzed to identify the peak points of signal fluctuation (such as the 3 peak points of current during start-up, which appear at 3 seconds, 7 seconds and 12 seconds, with values of 5A, 4.8A and 4.5A). The installation state data (such as the 3 speed peaks of the conveyor belt: 1.2m / s, 1.1m / s, 1.0m / s) and the equipment operating parameters (such as the 3 voltage peaks of the motor: 220V, 215V, 210V) of the corresponding stage are extracted from the start-up data segment to ensure that the number of peak points is consistent with the signal segment.

[0092] In another operation process of the embodiment of the application, the difference between the feedback signal segment and the installation state data is: sequentially compared according to the peak point order, the first peak point difference value = 5A-1.2m / s (Note: the units need to be converted into dimensionless values for calculation, for example, calculated as a percentage of the standard value), the second peak point difference value = 4.8A-1.1m / s, the third peak point difference value = 4.5A-1.0m / s, and the absolute value average of the three difference values is taken as the first difference value in the starting stage. The difference between the feedback signal segment and the equipment operating parameter is: also sequentially according to the peak point order, the first peak point difference value = 5A-220V (converted into dimensionless for calculation), the second peak point difference value = 4.8A-215V, the third peak point difference value = 4.5A-210V, and the absolute value average is taken as the second difference value in the starting stage. The total difference value in the starting stage = (the first difference value + the second difference value) / 2.

[0093] In another operation process of the embodiment of the application, for the "mainboard" assembly: the signal value (such as vibration frequency 20Hz) in the mainboard assembly period in the running signal segment is extracted, and the material attribute parameter (mainboard thickness 2mm) and the installation state data (installation position deviation 0.1mm) in the real-time assembly data are corresponded. First, the difference value between the signal value and the material attribute parameter (20Hz-2mm, converted into dimensionless) is calculated, and then the difference value between the signal value and the installation state data (20Hz-0.1mm, converted into dimensionless) is calculated, and the absolute value average of the two difference values is taken as the difference value of the mainboard assembly. 1) For the "heat sink" assembly: similarly, the signal value (vibration frequency 18Hz) is extracted, the difference value with the heat sink material attribute (area 10cm 2 ) is calculated, and the difference value with the installation state (fitting degree 95%) is calculated, and the average value is taken as the difference value of the heat sink assembly. 2) For the "housing" assembly: repeat the above steps to calculate the difference value of the housing assembly. The total difference value in the running stage = (the difference value of the mainboard assembly + the difference value of the heat sink assembly + the difference value of the housing assembly) / 3.

[0094] It should be pointed out that the stable value part is extracted from the pause signal segment, that is, the signal value (such as current 0.5A) after the equipment is completely stationary (such as 8-10 seconds after the pause instruction is issued); the static value part is extracted from the last set of real-time assembly data before the pause (such as the last recorded conveyor belt speed 0m / s, torque 0N·m). The difference value in the pause stage = signal stable value-data static value (converted into dimensionless for calculation, such as 0.5A-0m / s).

[0095] Step S4: comparing the running deviation amount with the preset running deviation threshold value, and recording the comparison result in real time; if the comparison result is within the preset assembly range, it is determined that the assembly process is in a normal state, and the assembly working condition is maintained until the production cycle ends.

[0096] Preferably, step S4 comprises the following steps:

[0097] Step S41: split the running deviation amount into a plate-shaped component associated deviation group, a column-shaped component associated deviation group and a frame type component associated deviation group according to the hardware component type, and split the preset running deviation threshold value into three group thresholds;

[0098] Step S42: for the plate-shaped component associated deviation group, sequentially compare the edge mounting hole position deviation and the surface interface plug-in state deviation with the corresponding group threshold value, and first compare the single deviation value, and then compare the cumulative value of the continuous three times deviation;

[0099] Step S43: for the column-shaped component associated deviation group, sequentially compare the axial installation depth deviation and the radial circumferential surface fitting deviation with the corresponding group threshold value, and first extract the peak part of the deviation, and then extract the stable part of the deviation;

[0100] Step S44: for the frame type component associated deviation group, sequentially compare the splicing part gap width deviation and the four corner horizontal height difference deviation with the corresponding group threshold value;

[0101] Step S45: if the comparison results of all groups are within the preset assembly range, lock the running parameter setting of the current assembly equipment, fix the conveying rhythm of the material supply, and keep the grabbing positioning path of the hardware component unchanged.

[0102] In an embodiment of the present application, one splitting rule is: 1) all deviation values generated in the plate-shaped component assembly process (such as edge mounting hole position deviation, surface interface plug-in state deviation) account for 40% of the running deviation amount; 2) all deviation values generated in the column-shaped component assembly process (such as axial installation depth deviation, radial circumferential surface fitting deviation) account for 30% of the running deviation amount; 3) all deviation values generated in the frame type component assembly process (such as splicing part gap width deviation, four corner horizontal height difference deviation) account for 30% of the running deviation amount.

[0103] For example, the preset running deviation threshold value is split into three group thresholds according to the above-mentioned proportion, assuming that the total threshold value is 0.5mm, the plate-shaped component group threshold value is 0.2mm, the column-shaped component group threshold value is 0.15mm, and the frame type component group threshold value is 0.15mm.

[0104] In another operation process of the embodiment of the application, the single deviation value is compared with the hole threshold value in the plate assembly group threshold value (such as 0.1 mm) by reading the real-time alignment deviation (such as 0.12 mm) of the edge mounting hole of the plate assembly, and if it exceeds, it is marked as abnormal. The cumulative value of the continuous three deviations is compared with the cumulative threshold value (such as 0.3 mm) by calculating the sum (0.36 mm) of the absolute values of the alignment deviations (such as 0.12 mm, 0.13 mm, 0.11 mm) collected continuously for three times, and if it exceeds, it is marked as abnormal. The peak part of the deviation is extracted, and the maximum value (such as 0.18 mm) is intercepted from the axial installation depth deviation curve, and compared with the axial peak threshold value (such as 0.2 mm) in the columnar assembly group threshold value. The stable part of the deviation is extracted, and the stable value (such as 0.1 mm) within 5 seconds in the deviation curve is intercepted, and compared with the axial stable threshold value (such as 0.15 mm). The gap width deviation of the splicing part is compared, the real-time gap width deviation (such as 0.12 mm) of the splicing part of the frame type assembly is read, and compared with the gap threshold value (such as 0.15 mm) in the frame type assembly group threshold value; the four corner horizontal height difference deviation comparison, read the horizontal height difference deviation (such as 0.08 mm) of the four corners, and compared with the height difference threshold value (such as 0.1 mm) in the frame type assembly group threshold value.

[0105] In another operation process of the embodiment of the application, the running parameters of the locking assembly device are locked, the current running parameters (such as the conveying belt speed 2 m / min, the clamping force 5 N) are saved as fixed values through the device control panel, and unauthorized modification is prohibited. The material feeding delivery rhythm is fixed, the material delivery interval is set to 10 seconds / piece, and the start and stop time of the delivery motor is controlled through the timer. The hardware component grabbing positioning path is maintained, and the current grabbing coordinates (such as X=100 mm, Y=50 mm, Z=30 mm) and motion trajectory are fixed in the mechanical arm control system, and the subsequent operation directly calls the path parameters.

[0106] It should be noted that after the parameters are locked, if adjustment is required, the administrator permission needs to be unlocked; the fixing of the delivery rhythm and the positioning path needs to be verified by restarting the device while still maintaining the current settings.

[0107] Step S5: If it is not within the preset assembly range, it is determined that the assembly process is in an abnormal state, and the assembly working condition is immediately stopped and abnormal information is recorded; the abnormal information is associated with the corresponding hardware component and the assembly station, and a household appliance production traceability record is generated.

[0108] Preferably, step S5 includes the following steps:

[0109] Step S51: When it is determined that the assembly process is in an abnormal state, a deceleration signal of the assembly device is sent; when the device running speed is reduced to one third of the initial speed, a stop signal is sent, and the device executing part remains unchanged after the stop signal is sent;

[0110] Step S52: Extract real-time assembly data before the abnormality occurs in the reverse order of the assembly process, first extract the hardware component installation state data of the last complete assembly, then extract the operating parameters of the corresponding assembly equipment, and finally extract the attribute parameters of the related assembly materials.

[0111] In an embodiment of the present application, when it is determined through the foregoing steps that the assembly process is in an abnormal state, the system sends a deceleration signal to the control unit of the assembly equipment. The deceleration signal is a low-level signal lasting for 5 seconds, which is transmitted to the driving module through the internal signal transmission line of the equipment.

[0112] In another operation process of the embodiment of the present application, if the initial running speed of the assembly equipment is 3 meters / minute, after the driving module receives the signal, the equipment is decelerated by gradually reducing the motor power frequency. During the deceleration process of the equipment, the speed sensor installed at the driven wheel of the conveyor belt monitors the running speed in real time, and feeds back the speed value to the control unit once every 0.5 seconds. When the speed is monitored to be reduced to 1 meter / minute (i.e. one third of the initial speed), the control unit immediately sends a stop signal. The stop signal is a high-level signal lasting for 2 seconds, which triggers the electromagnetic brake device of the equipment to act, so that the execution components such as the conveyor belt and the mechanical arm stop moving. After the stop signal is sent, the brake device remains in the locked state, ensuring that the spatial position of the execution components does not change (for example, the end of the mechanical arm stays at a height of 50 mm from the assembly platform, and the deviation of a certain point on the surface of the conveyor belt from the positioning reference line is not more than ±1 mm).

[0113] In another operation process of the embodiment of the present application, the real-time assembly data before the abnormality occurs is extracted in the reverse order of the assembly process (for example, the reverse order of “shell clamping → circuit connection → mainboard installation” is “mainboard installation → circuit connection → shell clamping”):

[0114] 1) Extract the hardware component installation state data of the last complete assembly. For example, if the abnormality occurs in the shell clamping process, the final state data of the mainboard installation is called, including the torque values of the four fixing screws of the mainboard (such as 3.2 N·m, 3.1 N·m, 3.3 N·m, 3.2 N·m), and the gap value between the mainboard and the case (such as 0.12 mm), which are derived from the storage records of the torque sensor and the laser displacement sensor of the installation station.

[0115] 2) Extract the operating parameters of the corresponding assembly equipment. Still taking the mainboard installation process as an example, the operating parameters of the mainboard installation mechanical arm are extracted, including the clamping force (such as 8 N), the moving speed (such as 0.5 meters / second), and the rotation angle (such as 90 degrees), which are derived from the real-time log of the mechanical arm control system.

[0116] 3) Extract the attribute parameters of the relevant assembly materials. For example, the material type of the mainboard (such as FR-4), the thickness (such as 1.6 mm), and the batch number (such as B20240518), which are derived from the archived information of the material scanning code record and the thickness detection sensor.

[0117] Preferably, step S5 further comprises the following steps:

[0118] Step S53: Record the position locking of the equipment execution component in the abnormal state, first record the spatial coordinate correlation information of the mechanical execution unit, then record the output state correlation information of the power supply unit, and mark the time difference value from the stop signal to the complete stop of the equipment;

[0119] Step S54: Integrate the extracted real-time assembly data, marked abnormal position and state information into an abnormal record file in chronological order, and store the abnormal record file in association with the unique identification of the corresponding household appliance device as abnormal information;

[0120] Step S55: Associate the abnormal information with the corresponding hardware components and assembly stations to generate the household appliance device production traceability record.

[0121] In one implementation manner of the embodiment of the application, the spatial coordinate correlation information of the mechanical execution unit is recorded: the spatial coordinates at the time of abnormal stop are read through the angle encoders installed at the joints of the mechanical arm and the coordinate sensors at the end. For example, the three-dimensional coordinates of the end of the mechanical arm are (X=200 mm, Y=150 mm, Z=80 mm), and the joint rotation angles are (θ1=30 degrees, θ2=60 degrees, θ3=45 degrees). After correlating these coordinates with the device reference coordinate system (with the lower left corner of the device as the origin), they are stored in the record. The output state correlation information of the power supply unit is recorded: the output parameters of the power components such as motors and air cylinders are read, such as the real-time current (such as 2.5 A) and voltage (such as 220 V) of the conveyor belt driving motor, and the working pressure (such as 0.6 MPa) of the pressing air cylinder, which are derived from the power monitoring module and pressure sensor of the device. The time difference value from the stop signal to the complete stop of the equipment is marked: the time when the stop signal is sent (such as 10:25:30.000) and the time when the equipment execution component completely stops (such as 10:25:32.500) are recorded through the timer of the control unit, and the time difference value of 2.5 seconds is calculated, accurate to 0.001 seconds.

[0122] In another operation process of the embodiment of the application, the file content sequentially contains: the abnormal occurrence time (such as 2024-06-1010:25:35), the data of each process before the abnormality (the installation state, the device parameters, and the material attributes arranged in reverse order), the mechanical execution unit coordinates, the power supply unit output state, and the stop time difference value. The file format adopts a text format, and each piece of information is marked with a timestamp (accurate to seconds).

[0123] It should be noted that the unique identifier obtained by scanning the barcode of the home appliance at the assembly line entrance (such as product serial number SN20240610001) is used to create an anomaly log file named "SN20240610001_anomaly log.txt". This file is stored in a designated path on the production data server (such as " / production records / anomaly data / 20240610 / "), and an association index between the identifier and the file path is established in the database. The traceability record clearly indicates that the anomaly occurred at workstation WS05, involving component PCB20240518003, along with the associated anomaly log file path and product serial number. The traceability record is presented in tabular form, containing fields such as "anomaly date, workstation number, component number, product serial number, anomaly type (marked here as assembly process anomaly), and record path". It is stored in the traceability management system's database and can be queried by product serial number or component number.

[0124] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0125] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A data fusion method for household appliance production quality safety traceability, characterized in that, The method is applied to the assembly link in the production process of household appliances, the household appliances are composed of multiple hardware components, the assembly link involves multiple assembly stations, and the method comprises the following steps: Step S1: In the household appliance assembly process, periodically and synchronously collect hardware component installation state data, assembly equipment operating parameters and assembly material attribute parameters, and fuse them into a real-time assembly data set; Step S2: Calculate the deviation between the real-time assembly data set and the standard assembly data set to determine the assembly deviation, and mark the assembly deviation as a quality characteristic parameter; Step S3: Adjust the operating state of the assembly equipment according to the quality characteristic parameter, and synchronously read the feedback signal of the assembly equipment during the adjustment process; determine the equipment operating deviation based on the difference between the feedback signal and the parameters in the real-time assembly data set; Step S4: Compare the operating deviation with the preset operating deviation threshold, and record the comparison result in real time; if the comparison result is within the preset assembly range, it is determined that the assembly process is in a normal state, and the assembly condition is maintained until the production cycle ends; Step S5: If it is not within the preset assembly range, it is determined that the assembly process is in an abnormal state, the assembly condition is immediately stopped, and abnormal information is recorded; the abnormal information is associated with the corresponding hardware component and assembly station to generate a household appliance production traceability record.

2. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, In step S1, the hardware component installation state data is collected as follows: Recognize the plate-shaped component, columnar component and frame-type component of the assembly hardware component type; When recognizing the plate-shaped component, the collection operation is executed in the order of first collecting the alignment data of the edge mounting hole, and then collecting the insertion state data of the surface interface; When recognizing the columnar component, the collection operation is executed in the order of first collecting the axial installation depth data, and then collecting the fit degree data of the radial circumference surface; When recognizing the frame-type component, the collection operation is executed in the order of first collecting the gap width data of the splicing part, and then collecting the horizontal height difference data of the four corners; Multiple continuous collections are performed on each collection point of each hardware component, and the average value of the multiple collection data is taken as the effective data of the collection point to determine the hardware component installation state data.

3. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, In step S1, the assembly equipment operating parameters are collected as follows: Determine the equipment type according to the main operation type of the assembly equipment in the current assembly process; when the main operation type is to provide driving force to move the parts, it is determined that the assembly equipment is a power supply type equipment; when the main operation type is to directly perform part grabbing, positioning or fastening operation, it is determined that the assembly equipment is a mechanical execution type equipment; If it is determined to be a power supply type equipment, collect the voltage value at the peak position of each power supply cycle after the equipment is started, and collect the current value at the valley position; adjust the collection time points of adjacent cycles in an equal ratio incremental manner, and synchronously record the temperature gradient data of the power supply line; If it is determined to be a mechanical execution type equipment, when performing linear motion, collect the speed value at 1 / 4, 1 / 2 and 3 / 4 positions of the stroke; when performing rotary motion, collect the torque value every interval of π / 4 radian according to the rotation angle; After each action cycle ends, measure the angle deviation value of the mechanical arm joint under the application of an equal calibration force.

4. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, The attribute parameters of the assembly material collected in step S1 are specifically as follows: The surface feature point arrangement rule of the assembly material is identified to determine the type of the assembly material. When the connecting line direction of adjacent feature points presents at least three different angles and has no obvious row and column alignment feature, it is determined that the assembly material is a dot matrix type. When the connecting line direction of adjacent feature points presents only two directions perpendicular to each other and forms a regular grid, it is determined that the assembly material is a chessboard type. If it is determined that the assembly material is a dot matrix type, the attribute parameters are sequentially collected from the edge feature points to the center feature points. If it is determined that the assembly material is a chessboard type, the attribute parameters of the odd row feature points are collected along the horizontal direction of the chessboard, and then the attribute parameters of the even column feature points are collected along the vertical direction of the chessboard. In the collection process of each row or each column of the chessboard type assembly material, an interval skipping collection method is used, in which the second feature point is skipped after the first feature point is collected, the fourth feature point is skipped after the third feature point is collected, and so on until the collection of the row or the column is completed.

5. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: The real-time assembly data group is split into an installation state data subgroup, an assembly equipment operation parameter subgroup, and an assembly material attribute parameter subgroup according to the data type, and the standard assembly data group is correspondingly split into three standard subgroups; Step S22: For the installation state data subgroup and the corresponding standard subgroup, the deviation is calculated in sequence according to the assembly sequence of the hardware components, first calculating the independent installation deviation of each component, and then calculating the cooperative installation deviation of adjacent components; Step S23: For the assembly equipment operation parameter subgroup and the corresponding standard subgroup, the deviation is calculated in sections according to the time nodes of equipment operation, and the parameter fluctuation deviation is calculated in the equipment start-up stage, stable operation stage, and pre-shutdown stage; Step S24: For the assembly material attribute parameter subgroup and the corresponding standard subgroup, the deviation is calculated according to the assembly and use sequence of the material, first calculating the initial attribute deviation when not assembled, and then calculating the dynamic attribute deviation in the assembly process; Step S25: The calculation results of the above three types of deviations are integrated, the installation state data deviation accounts for 40%, the assembly equipment operation parameter deviation accounts for 30%, and the assembly material attribute parameter deviation accounts for 30%, and the assembly deviation is obtained by weighting, and the assembly deviation is marked as a quality characteristic parameter.

6. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, In step S3, the running state of the assembly equipment is adjusted according to the quality characteristic parameter, and the feedback signals of the assembly equipment are read synchronously during the adjustment, including: The type of the hardware component associated with the quality characteristic parameter is identified. When a plate-shaped component is associated, the running state of the assembly equipment is adjusted to the transmission part related to the assembly of the plate-shaped component. When a columnar component is associated, the adjustment is directed to the positioning part related to the assembly of the columnar component. When a frame type component is associated, the adjustment is directed to the splicing part related to the assembly of the frame type component. For the transmission part associated with the plate-shaped component, the adjustment is performed in the order of first reducing the running speed of the conveyor belt and then reducing the spacing of the clamping device. After each adjustment, the current state is maintained until the delivery cycle of a plate-shaped component is completed. For the positioning part associated with the columnar component, the adjustment is performed in the order of first increasing the rotation platform speed and then increasing the pressure of the pressing device. After each adjustment, the rotation platform is stopped until it completes a half rotation. For the splicing part associated with the frame type component assembly, adjust in the order of first slowing down the splicing speed and then adjusting the splicing alignment accuracy, and keep the current state after each adjustment until the splicing operation of one frame edge is completed; Read the initial feedback signal of the assembly equipment before the adjustment is started; Read the feedback signal once at the transfer belt turning node when adjusting the transmission part, read the feedback signal once at the moment when the pressing device contacts the columnar component when adjusting the positioning part, and read the feedback signal once at the frame edge butt joint overlap when adjusting the splicing part.

7. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, In step S3, the difference between the feedback signal and the parameters in the real-time assembly data set is used to determine the equipment operation deviation, which includes: The feedback signal is divided into start-up stage signal segment, running stage signal segment and pause stage signal segment according to the time node of the assembly process, and the parameters in the real-time assembly data set are correspondingly divided into three stages; For the start-up stage, first calculate the difference between the feedback signal segment and the installation state data of the corresponding stage, and then calculate the difference between the signal segment and the assembly equipment operation parameters of the corresponding stage, both of which are compared in turn according to the peak points of signal fluctuation; For the running stage, according to the assembly sequence of hardware components, the difference between the feedback signal segment and the real-time assembly data parameters of each component assembly is calculated in turn, first comparing the material attribute parameter difference, and then comparing the installation state data difference; For the pause stage, calculate the difference between the feedback signal segment and the last set of real-time assembly data parameters before the pause, and when comparing, first extract the stable value part of the signal, and then extract the static value part of the data parameters; The difference calculation results of the three stages are fused according to the proportions of 20% for the start-up stage, 60% for the running stage and 20% for the pause stage to obtain the equipment operation deviation.

8. The data fusion method for home appliance production quality safety traceability according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: The running deviation is divided into plate-shaped component associated deviation group, columnar component associated deviation group and frame type component associated deviation group according to the type of hardware components, and the preset running deviation threshold is correspondingly divided into three group thresholds; Step S42: For the plate-shaped component associated deviation group, compare in turn according to the edge mounting hole alignment deviation, surface interface insertion state deviation, first compare the single deviation value, and then compare the cumulative value of three consecutive deviations; Step S43: For the columnar component associated deviation group, compare in turn according to the axial installation depth deviation and the radial circumference surface fit deviation, first extract the peak part of the deviation, and then extract the stable part of the deviation; Step S44: For the frame type component associated deviation group, compare in turn according to the splicing part gap width deviation and the four corner horizontal height difference deviation; Step S45: If the comparison results of all groups are within the preset assembly range, lock the running parameter settings of the current assembly equipment, fix the delivery rhythm of the material supply, and keep the grabbing and positioning path of the hardware components unchanged.

9. The data fusion method for home appliance production quality safety traceability according to claim 8, characterized in that, Step S5 includes the following steps: Step S51: When it is determined that the assembly process is in an abnormal state, send a deceleration signal of the assembly equipment; when the equipment running speed is reduced to one third of the initial speed, send a stop signal, and keep the current position of the equipment execution part unchanged after the stop signal is sent; Step S52: Extract the real-time assembly data before the abnormality occurs in the reverse order of the assembly process, first extract the hardware component installation state data of the last complete assembly, then extract the operating parameters of the corresponding assembly equipment, and finally extract the attribute parameters of the related assembly materials.

10. The data fusion method for home appliance production quality safety traceability according to claim 9, characterized in that, Step S5 further includes the following steps: Step S53: Perform position locking recording on the equipment execution components in the abnormal state, first record the spatial coordinate correlation information of the mechanical execution unit, then record the output state correlation information of the power supply unit, and mark the time difference from the stop signal issuance to the complete stop of the equipment; Step S54: Integrate the extracted real-time assembly data, marked abnormal position and state information into an abnormal record file in chronological order, and store the abnormal record file and the unique identification of the corresponding household appliance equipment in association as abnormal information; Step S55: Associate the abnormal information with the corresponding hardware components and assembly stations to generate household appliance production traceability records.

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